Masked deep reinforcement learning for virtual network embedding on elastic optical networks

Deep reinforcement learning (DRL) with invalid action masking is applied to the optimization problem of virtual optical network embedding (VONE) over elastic optical networks (EON). Separate DRL agents are trained on the nodemapping task, link-mapping task, and overall VONE task. Their blocking probability performance is compared with a spectral fragmentation-aware VONE heuristic. All three DRL agents achieve lower blocking probability than the heuristic across low and high traffic loads

Distributed Relay Selection for Heterogeneous UAV Communication Networks Using A Many-to-Many Matching Game Without Substitutability

This paper proposes a distributed multiple relay selection scheme to maximize the satisfaction experiences of unmanned aerial vehicles (UAV) communication networks. The multi-radio and multi-channel (MRMC) UAV communication system is considered in this paper. One source UAV can select one or more relay radios, and each relay radio can be shared by multiple source UAVs equally. Without the center controller, source UAVs with heterogeneous requirements compete for channels dominated by relay radios. In order to optimize the global satisfaction performance, we model the UAV communication network as a many-to-many matching market without substitutability. We design a potential matching approach to address the optimization problem, in which the optimizing of local matching process will lead to the improvement of global matching results. Simulation results show that the proposed distributed matching approach yields good matching performance of satisfaction, which is close to the global optimum result. Moreover, the many-to-many potential matching approach outperforms existing schemes sufficiently in terms of global satisfaction within a reasonable convergence time.Comment: 6 pages, 4 figures, conferenc

The Political Personality of 2020 Democratic Vice-Presidential Nominee Kamala Harris

This working paper presents the results of an indirect assessment of the personality of U.S. senator Kamala Harris, Democratic vice-presidential nominee in the 2020 U.S. presidential election, from the conceptual perspective of personologist Theodore Millon. Psychodiagnostically relevant data about Harris were collected from biographical sources and media reports and synthesized into a personality profile using the Millon Inventory of Diagnostic Criteria (MIDC), which yields 34 normal and maladaptive personality classifications congruent with DSM-III-R, DSM-IV, and DSM-5. The personality profile yielded by the MIDC was analyzed in accordance with interpretive guidelines provided in the MIDC and Millon Index of Personality Styles manuals. Harris’s primary personality pattern was found to be Dominant/asserting (a measure of aggressiveness), complemented by secondary Ambitious/confident and Outgoing/congenial patterns — measures of narcissism and extraversion, respectively. In summary, Harris’s personality composite can be characterized as high-dominance charismatic — charismatic by virtue of the elevated Ambitious–Outgoing amalgam. Dominant individuals enjoy the power to direct others and to evoke obedience and respect; they are tough and unsentimental and often make effective leaders. Ambitious individuals are bold, competitive, and self-assured; they easily assume leadership roles, expect others to recognize their special qualities, and sometimes act as though entitled. Outgoing individuals are dramatic attention-getters who thrive on being the center of social events, go out of their way to be popular with others, and have confidence in their social abilities. Harris’s major personality strengths in a political role are her confident assertiveness and personal charisma. Her major personality-based shortcoming is likely to be a predisposition to occasional lapses in emotional restraint or self-discipline

DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata

Current Dynamic Texture Synthesis (DyTS) models in the literature can synthesize realistic videos. However, these methods require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis process. We propose Dynamic Neural Cellular Automata (DyNCA), a framework for real-time and controllable dynamic texture synthesis. Our method is built upon the recently introduced NCA models, and can synthesize infinitely-long and arbitrary-size realistic texture videos in real-time. We quantitatively and qualitatively evaluate our model and show that our synthesized videos appear more realistic than the existing results. We improve the SOTA DyTS performance by $2\sim 4$ orders of magnitude. Moreover, our model offers several real-time and interactive video controls including motion speed, motion direction, and an editing brush tool